The present invention relates generally to mobile communications, and, more particularly, to multi-stream receivers for multiple-input multiple-output MIMO systems and their extensions.
As discussed in co-pending, commonly owned Published Applications Serial No. 20080225976, the content of which is incorporated by reference, mobile wireless communications systems employing multiple transmit and receive antennas have received much attention lately. This is due—in part—to the fact that the capacity of such systems increases linearly with the minimum of the number of transmit and receive antennas without requiring any additional power or bandwidth. Of the known signal detection schemes employed in contemporary MIMO systems, a maximum-likelihood (ML) scheme is one of the most attractive. Unfortunately however, ML schemes exhibit a computational complexity that makes its implementation infeasible or impractical for large systems.
As further discussed in co-pending, commonly owned Published Application No. 20080069262, the content of which is incorporated by reference, a known brute force maximum likelihood ML reception method for two streams involves listing all possible pairs for symbols, evaluating the metric for each pair, using the metrics to determine the exact max-log LLRs (maximum log likelihood ratios) for all the bits and decoding the codeword(s) using the computed LLRs. Although the brute force ML method provides optimal demodulation it is highly complex.
State of the art demodulators have used the Deterministic Sequential Monte-Carlo (D-SMC) multi stream receiver or the successive interference cancellation (SIC) receiver. Complexity reduction is achieved with the D-SMC method by computing the soft output for each coded bit over only a reduced set of hypotheses. The price paid for this complexity reduction is that the D-SMC suffers from the “missing candidate problem”, in that the hypotheses (or candidates) necessary for computing the soft outputs for some of the bits may not be present in the reduced set. This missing candidate problem can cause significant degradation in the performance particularly if the reduced set is relatively small compared to the set of all hypotheses. Heuristic techniques to alleviate this problem in the D-SMC have also been proposed but such techniques require a lot of system or scenario specific fine tuning and may not work well under across all conditions.
The SIC receiver is a sequential receiver where one stream is first decoded, re-constructed and then subtracted from the received signal before decoding the remaining streams. The soft output for each stream is obtained after assuming the un-decoded streams to be Gaussian interferers, which can lead to performance degradation and the sequential process results in increased latency.